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TAMING UNCERTAINTY: THE LIMITS TO QUANTIFICATION1

Published online by Cambridge University Press:  01 February 2016

Andreas Tsanakas ,
M. Bruce Beck  and
Michael Thompson
Andreas Tsanakas
Affiliation:
Faculty of Actuarial Science and Insurance, Cass Business School, City University London, London, United Kingdom, E-Mail: a.tsanakas.1@city.ac.uk
M. Bruce Beck
Affiliation:
Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom, E-Mail: mbrucebeck@gmail.com
Michael Thompson
Affiliation:
International Institute for Applied Systems Analysis, Laxenburg, Austria, E-Mail: thompson@iiasa.ac.at
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Extract

Taming the beast of uncertainty has been the grand project to which actuaries have dedicated much of their energy and skill over at least the last 50 years – roughly the time since, in Hans Bühlmann's (1989) famous term, “Actuaries of the Second Kind” emerged.

Keywords

Model uncertaintymodel riskplural rationalitiescultural risk theory
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 46 , Issue 1 , January 2016 , pp. 1 - 7
Copyright
Copyright © Astin Bulletin 2016 

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Footnotes

1.

This note draws on the report of the Institute and Faculty of Actuaries Working Party on Model Risk (2015), to which the authors contributed. We thank members of the Working Party, as well as Andrew Hitchcox and Malcolm Kemp, for their invaluable feedback. We also thank the Editor for helpful suggestions.

References

Barrieu, P. and Scandolo, G. (2015) Assessing financial model risk. European Journal of Operational Research, 242 (2), 546–556.CrossRefGoogle Scholar
Beck, M.B. (2014) Handling uncertainty in environmental models at the science-policy-society interfaces. In Error and Uncertainty in Scientific Practice (eds. Boumans, M., Hon, G. and Petersen, A. C.), pp. 97–135. London: Pickering & Chatto.Google Scholar
Bignozzi, V. and Tsanakas, A. (2015) Parameter uncertainty and residual estimation risk. Journal of Risk and Insurance, forthcoming.CrossRefGoogle Scholar
Board of Governors of the Federal Reserve System (2011) SR 11–7: Guidance on Model Risk Management. Available: http://www.federalreserve.gov/bankinforeg/srletters/sr1107a1.pdf.Google Scholar
Bronk, R. (2011) Uncertainty, modelling monocultures and the financial crisis. Business Economist, 42 (2), 5.Google Scholar
Bühlmann, H. (1989) Actuaries of the third kind? ASTIN Bulletin, 19 (S1), 5–6.Google Scholar
Cairns, A.J. (2000) A discussion of parameter and model uncertainty in insurance. Insurance: Mathematics and Economics, 27 (3), 313–330.Google Scholar
Cont, R., Deguest, R. and Scandolo, G. (2010) Robustness and sensitivity analysis of risk measurement procedures. Quantitative Finance, 10 (6), 593–606.CrossRefGoogle Scholar
D'Arcy, S. (2005) On Becoming an Actuary of the Fourth Kind. Proceedings of the Casualty Actuarial Society (Presidential Address, November 14, 2005).Google Scholar
Donnelly, C. and Embrechts, P. (2010) The devil is in the tails: Actuarial mathematics and the subprime mortgage crisis. ASTIN Bulletin, 40 (01), 1–33.CrossRefGoogle Scholar
Greenspan, A. (2013) The map and the territory. Risk, Human Nature and the Future of Forecasting. USA:Penguin Press.Google Scholar
Ingram, D., Tayler, P. and Thompson, M. (2012) Surprise, surprise: From neoclassical economics to e-life. ASTIN Bulletin, 42 (2), 389–411.Google Scholar
Jones, S. (2008) Alphaville: Rating cows. Financial Times, 23 October 2008.Google Scholar
Lane, D.A. and Maxfield, R.R. (2005) Ontological uncertainty and innovation. Journal of Evolutionary Economics, 15 (1), 3–50.CrossRefGoogle Scholar
MacKenzie, D. and Spears, T. (2014) ‘A device for being able to book P& L’: The organizational embedding of the gaussian copula. Social Studies of Science, 44 (3), 418–440.CrossRefGoogle Scholar
Thompson, M., Ellis, R. and Wildavsky, A. (1990) Cultural Theory. Boulder: Westview Press.Google Scholar
Tsanakas, A., Beck, M.B., Ford, T., Thompson, M. and Ye, I. (2014) Model risk and culture. Actuary Magazine, December 2014.Google Scholar
Turner, A. (2009) The turner review: A regulatory response to the global banking crisis. Financial Services Authority; available: www.fsa.gov.uk/pubs/other/turner_review.pdf.Google Scholar
Verweij, M. and Thompson, M. (eds.) (2006) Clumsy Solutions for a Complex World: Governance, Politics and Plural Perceptions. UK: Palgrave Macmillan.CrossRefGoogle Scholar
Working Party on Model Risk. (2015) Model risk: Daring to open the black box. Presented to the Institute and Faculty of Actuaries on 23 March 2015. Available: www.actuaries.org.uk/sites/all/files/documents/pdf/model-risk-working-party-paper.pdf.Google Scholar
Ziegel, J.F. (2014) Coherence and elicitability. Mathematical Finance, forthcoming. DOI: 10.1111/mafi.12080.CrossRefGoogle Scholar